From AI security to ethical AI security: a comparative risk-mitigation framework for classical and hybrid AI governance
Abstract As Artificial Intelligence (AI) systems evolve from classical to hybrid classical-quantum architectures, traditional notions of security—mainly centered on technical robustness—are no longer sufficient. This study aims to provide an integrated security ethics compliance framework that bridges technical and ethical dimensions across the AI lifecycle. By adopting a security ethics-by-design approach, the framework introduces mitigation measures in relation to key ethical principles capable of addressing emerging risks and considering AI governance needs in the initial AI design and development phases. This study proposes a novel framework, currently absent from the literature, to address security ethics challenges in both classical and hybrid systems. Key contributions include the integration of post-quantum and quantum cryptography, particularly homomorphic encryption, to ensure long-term privacy and security in hybrid AI. The framework also includes bias test
Abstract As Artificial Intelligence (AI) systems evolve from classical to hybrid classical-quantum architectures, traditional notions of security—mainly centered on technical robustness—are no longer sufficient. This study aims to provide an integrated security ethics compliance framework that bridges technical and ethical dimensions across the AI lifecycle. By adopting a security ethics-by-design approach, the framework introduces mitigation measures in relation to key ethical principles capable of addressing emerging risks and considering AI governance needs in the initial AI design and development phases. This study proposes a novel framework, currently absent from the literature, to address security ethics challenges in both classical and hybrid systems. Key contributions include the integration of post-quantum and quantum cryptography, particularly homomorphic encryption, to ensure long-term privacy and security in hybrid AI. The framework also includes bias testing and explainable AI techniques to promote fairness and explainability, and to prevent safety-related vulnerabilities—such as algorithmic bias—from serving as vectors for malicious, discriminatory attacks. Ultimately, it provides a preliminary roadmap for embedding ethical security considerations throughout the lifecycle of classical and hybrid AI systems.
Executive Summary
This article proposes a risk-mitigation framework for classical and hybrid AI governance, integrating technical and ethical dimensions across the AI lifecycle. The framework introduces mitigation measures aligned with key ethical principles, addressing emerging risks and considering AI governance needs from the initial design and development phases. It incorporates post-quantum and quantum cryptography, bias testing, and explainable AI techniques to ensure long-term privacy, security, fairness, and explainability.
Key Points
- ▸ Integration of technical and ethical dimensions in AI governance
- ▸ Introduction of a security ethics-by-design approach
- ▸ Incorporation of post-quantum and quantum cryptography for long-term security
Merits
Comprehensive Framework
The proposed framework provides a holistic approach to AI security and ethics, addressing various aspects of AI governance and risk mitigation.
Innovative Use of Cryptography
The integration of post-quantum and quantum cryptography, particularly homomorphic encryption, ensures long-term privacy and security in hybrid AI systems.
Demerits
Complexity and Scalability
The implementation of the proposed framework may be complex and challenging to scale, particularly for smaller organizations or less mature AI systems.
Expert Commentary
The proposed framework represents a significant step forward in addressing the complex interplay between technical and ethical dimensions in AI governance. By integrating security ethics-by-design and incorporating innovative cryptographic techniques, the framework provides a robust foundation for ensuring long-term privacy, security, and fairness in classical and hybrid AI systems. However, its implementation will require careful consideration of complexity and scalability, as well as ongoing evaluation and adaptation to emerging risks and challenges.
Recommendations
- ✓ Organizations should adopt a security ethics-by-design approach to AI governance, incorporating technical and ethical considerations from the initial design and development phases.
- ✓ Regulatory bodies should develop more comprehensive and integrated policies for AI governance, addressing both technical and ethical aspects of AI development and deployment.